Open Access

ARTICLE

Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics

by Noman Shabbir¹, Lauri Kütt¹, Muhammad Jawad², Oleksandr Husev¹, Ateeq Ur Rehman³, Akber Abid Gardezi⁴, Muhammad Shafiq⁵, Jin-Ghoo Choi^5,*

1 Department of Electrical Power Engineering & Mechatronics, Tallinn University of Technology, Tallinn, 19086, Estonia
2 Department of Electrical and Computer Engineering, COMSATS University Islamabad (Lahore Campus), 54000, Pakistan
3 Department of Electrical Engineering, Government College University, Lahore, 54000, Pakistan
4 Department of Computer Science, COMSATS University Islamabad, Islamabad, 45550, Pakistan
5 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Korea

* Corresponding Author: Jin-Ghoo Choi. Email:

Computers, Materials & Continua 2022, 72(1), 1017-1033. https://doi.org/10.32604/cmc.2022.024576

Received 22 October 2021; Accepted 10 December 2021; Issue published 24 February 2022

Abstract

Wind energy is featured by instability due to a number of factors, such as weather, season, time of the day, climatic area and so on. Furthermore, instability in the generation of wind energy brings new challenges to electric power grids, such as reliability, flexibility, and power quality. This transition requires a plethora of advanced techniques for accurate forecasting of wind energy. In this context, wind energy forecasting is closely tied to machine learning (ML) and deep learning (DL) as emerging technologies to create an intelligent energy management paradigm. This article attempts to address the short-term wind energy forecasting problem in Estonia using a historical wind energy generation data set. Moreover, we taxonomically delve into the state-of-the-art ML and DL algorithms for wind energy forecasting and implement different trending ML and DL algorithms for the day-ahead forecast. For the selection of model parameters, a detailed exploratory data analysis is conducted. All models are trained on a real-time Estonian wind energy generation dataset for the first time with a frequency of 1 h. The main objective of the study is to foster an efficient forecasting technique for Estonia. The comparative analysis of the results indicates that Support Vector Machine (SVM), Non-linear Autoregressive Neural Networks (NAR), and Recurrent Neural Network-Long-Term Short-Term Memory (RNN-LSTM) are respectively 10%, 25%, and 32% more efficient compared to TSO's forecasting algorithm. Therefore, RNN-LSTM is the best-suited and computationally effective DL method for wind energy forecasting in Estonia and will serve as a futuristic solution.

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Keywords

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